About the Role
Forecasting sits underneath much of North: projected spend, budgets, and the commitment purchases we make on customers' behalf all depend on knowing where a customer's usage is headed. When a forecast is off, customers feel it, either in missed savings or in commitments that go unused.
You'll join the Services team and own the models behind North's internal forecasting service, working with your teammates on the service that delivers forecasts across North's features. You'll shape how the service evolves, from how models are trained and evaluated to how forecast quality is monitored in production. You'll also work closely with data scientists and the teams who rely on forecasts, so results are clear to the people making decisions from them.
We're hiring for this role without a fixed level. We'll level you based on experience and on how you work, using our engineering ladder.
What You'll Do
- Build and improve the time series models behind projected spend, budgets, and commitment recommendations, across customers with very different sizes and usage patterns.
- Partner with your Services teammates to take models into production as a reliable shared service with clear interfaces.
- Shape the forecasting service's architecture, including how models are trained, versioned, evaluated, and served.
- Improve how we evaluate, backtest, and monitor forecast accuracy over time.
- Build proactive detection and alerting for unusual forecasts and model drift, so issues surface before they affect a decision.
- Quantify forecast uncertainty, such as with prediction intervals, so the features that use forecasts can account for it.
- Work with data scientists to turn forecast results and model changes into findings product and business teams can act on.
- Work with the Commitments, Visibility, and Data teams on what each feature needs from a forecast.
What We're Looking For
- 4+ years of software or machine learning engineering experience, including shipping time series models to production and owning them after launch.
- Deep experience with time series forecasting across classical statistical methods and machine learning approaches, and judgment about when each fits.
- Experience with time-aware validation, backtesting, and forecast accuracy metrics, and an understanding of their tradeoffs.
- Experience monitoring production models, including drift, anomaly detection, and retraining.
- Fluency in Python and SQL, and experience with large datasets.
- Experience collaborating with data scientists, and explaining model behavior and uncertainty to people outside engineering.
- Pragmatic, informed use of AI coding tools.
Nice to Have
- Experience working as a data scientist, or doing analysis work alongside ML engineering.
- Experience with probabilistic forecasting.
- Experience forecasting usage, demand, or spend across many accounts with different patterns.
- Experience with cloud usage, billing, or pricing data.
- Experience with AWS ML services.
- Familiarity with FinOps concepts.
Work Setup
Hybrid role based in New York City, with an office in Dumbo, Brooklyn
Team members typically work in the office 2–3 days per week
OR Remote US
Benefits
- Unlimited PTO
- 16 weeks of paid parental leave (birthing parents receive an additional 4 weeks at 50% pay)
- Two company-wide breaks per year: one around Labor Day and one at the end of the year
- Aetna Medical plan with the majority of the premium covered by North
- Dental and vision insurance
- Flexible spending account (FSA) for health expenses and commuter benefits
- 30 day sabbatical every 4 years